Assume, I have a classifier (It could be any of the standard classifiers like decision tree, random forest, logistic regression .. etc.) for fraud detection using the below code
library(randomForest) rfFit = randomForest(Y ~ ., data = myData, ntree = 400) # A very basic classifier
Say, Y is a binary outcome - Fraud/Not-Fraud
Now, I have predicted on a unseen data set.
pred = predict(rfFit, newData)
Then I have obtained the feedback from the investigation team on my classification and found that I have made a mistake of classifying a fraud as Non-Fraud (i.e. One False Negative). Is there anyway that I can let my algorithm understand that it has made a mistake? i.e. Any way of adding a feedback loop to the algorithm so that it can correct the mistakes?
One option I can think from top of my head is build an
adaboost classifier so that the new classifier corrects the mistake of the old one. or I have heard something of
Incremental Learning or
Online learning. Are there any existing implementations (packages) in
Is it the right approach? or Is there any other way to tweak the model instead of building it from the scratch?